Census metadata driven data collection monitoring: The Ethiopian experience

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Gait Analysis in Progressive Supranuclear Palsy Phenotypes

The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD

Temporal muscle thickness and survival in patients with amyotrophic lateral sclerosis

Temporal muscle thickness (TMT) is a new potential MRI biomarker, which has shown prognostic relevance in neuro-oncology. We aim at investigating the potential prognostic value of TMT in patients with Amyotrophic Lateral Sclerosis (ALS). We retrospectively evaluated 30 ALS patients, whose clinical, Magnetic Resonance Imaging (MRI) and Electrodiagnostic testing (EDX) data were available, in comparison to age-matched 30 healthy subjects. TMT calculated on T1-weighted MR images was significantly lower in ALS patients than in healthy subjects (p < 0.001), correlating with the ALS Functional Rating Scale (FRS) (p:0.018) and compound motor action potential (CMAP) (p:0.012) in the patients group. Multivariate analysis of overall survival (OS) showed that the only parameters that remained significant were TMT (p:0.002, OR 0.45, 95%vCI: 0.28-0.75) and ALS FRS-R (p:0.023, OR: 0.80, 95%CI: 0.67-0.92). TMT seems to be a promising surrogate biomarker of survival and functional status in ALS. Our data deserve further investigations in multicenter and prospective trials

Performing a short sway to distinguish Parkinsonisms

The objective of the present study was to analyze postural stability of patients with different Parkinsonisms by verifying the ability of a short sway could distinguish Progressive Supranuclear Palsy (PSP), atypical parkinsonism, from the typical Parkinson’s disease (PD). Postural stability was investigated by using a stabilometric analysis system during quiet stance with eyes open in a trial of 5/6s. The study population comprised 30 participants (15 PSP patients and 15 patients with recent diagnosis of PD (De Novo PD)). Univariate statistical analysis was used to compare PSP patients and De Novo PD patients. Findings indicated that balance and postural stability were poorer in PSP patients than De Novo PD. PSP patients exhibited increased measures of medio-lateral (M-L) instability, as attested by augmented M-L sway, M-L range and radius. Then, sway variables were given as input to machine learning algorithms: Decision Tree (DT), Support Vector Machine (SVM) and Naïve-Bayes (NB). Overall, machine learning classifiers showed evaluation metrics about the 70%. DT achieved the highest accuracy (73.0%) and the highest AUCROC (75.0%). SVM achieved the best sensitivity (67.0%). Application of predictive models to sway data revealed that machine learning analysis was able to classify patients with different Parkinsonism. The severity of PSP seems to be particularly associated with postural sway

Wearable sensors for assessing disease severity and progression in Progressive Supranuclear Palsy

Introduction: Progressive supranuclear palsy (PSP) is an atypical parkinsonism characterized by prominent gait and postural impairment. The PSP rating scale (PSPrs) is a clinician-administered tool to evaluate disease severity and progression. More recently, digital technologies have been used to investigate gait parameters. Therefore, object of this study was to implement a protocol using wearable sensors evaluating disease severity and progression in PSP. Methods: Patients were evaluated with the PSPrs as well as with three wearable sensors located on the feet and lumbar area. Spearman coefficient was used to assess the relationship between PSPrs and quantitative mea-surements. Furthermore, sensor parameters were included in a multiple linear regression model to assess their ability in predicting the PSPrs total score and sub-scores. Finally, differences between baseline and three-month follow-up were calculated for PSPrs and each quantitative variable. The significance level in all analyses was set at <= 0.05.Results: Fifty-eight evaluations from thirty-five patients were analyzed. Quantitative measurements showed multiple significant correlations with the PSPrs scores (r between 0.3 and 0.7; p < 0.05). Linear regression models confirmed the relationships. After three months visit, significant worsening from baseline was observed for cadence, cycle duration and PSPrs item 25, while PSPrs item 10 showed a significant improvement.Conclusion: We propose wearable sensors can provide an objective, sensitive quantitative evaluation and im-mediate notification of gait changes in PSP. Our protocol can be easily introduced in outpatient and research settings as a complementary tool to clinical measures as well as an informative tool on disease severity and progression in PSP